# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# limitations under the License.
# ============================================================================
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from npu_bridge.npu_init import *
import sklearn
import sklearn.ensemble
import gc
from sklearn.preprocessing import StandardScaler
import numpy as np

class KerasWrap(object):
    """ A wrapper that allows us to set parameters in the constructor and do a reset before fitting.
    """
    def __init__(self, model, epochs, flatten_output=False):
        self.model = model
        self.epochs = epochs
        self.flatten_output = flatten_output
        self.init_weights = None
        self.scaler = StandardScaler()
        
    def fit(self, X, y, verbose=0):
        if self.init_weights is None:
            self.init_weights = self.model.get_weights()
        else:
            self.model.set_weights(self.init_weights)
        self.scaler.fit(X)
        return self.model.fit(X, y, epochs=self.epochs, verbose=verbose)

    def predict(self, X):
        X = self.scaler.transform(X)
        if self.flatten_output:
            return self.model.predict(X).flatten()
        else:
            return self.model.predict(X)


# This models are all tuned for the corrgroups60 dataset

def corrgroups60__lasso():
    """ Lasso Regression
    """
    return sklearn.linear_model.Lasso(alpha=0.1)

def corrgroups60__ridge():
    """ Ridge Regression
    """
    return sklearn.linear_model.Ridge(alpha=1.0)

def corrgroups60__decision_tree():
    """ Decision Tree
    """

    # max_depth was chosen to minimise test error
    return sklearn.tree.DecisionTreeRegressor(random_state=0, max_depth=6)

def corrgroups60__random_forest():
    """ Random Forest
    """
    return sklearn.ensemble.RandomForestRegressor(100, random_state=0)

def corrgroups60__gbm():
    """ Gradient Boosted Trees
    """
    import xgboost

    # max_depth and learning_rate were fixed then n_estimators was chosen using a train/test split
    return xgboost.XGBRegressor(max_depth=6, n_estimators=50, learning_rate=0.1, n_jobs=8, random_state=0)

def corrgroups60__ffnn():
    """ 4-Layer Neural Network
    """
    from keras.models import Sequential
    from keras.layers import Dense

    model = Sequential()
    model.add(Dense(32, activation='relu', input_dim=60))
    model.add(Dense(20, activation='relu'))
    model.add(Dense(20, activation='relu'))
    model.add(Dense(1))

    model.compile(optimizer='adam',
                loss='mean_squared_error',
                metrics=['mean_squared_error'])

    return KerasWrap(model, 30, flatten_output=True)


def independentlinear60__lasso():
    """ Lasso Regression
    """
    return sklearn.linear_model.Lasso(alpha=0.1)

def independentlinear60__ridge():
    """ Ridge Regression
    """
    return sklearn.linear_model.Ridge(alpha=1.0)

def independentlinear60__decision_tree():
    """ Decision Tree
    """

    # max_depth was chosen to minimise test error
    return sklearn.tree.DecisionTreeRegressor(random_state=0, max_depth=4)

def independentlinear60__random_forest():
    """ Random Forest
    """
    return sklearn.ensemble.RandomForestRegressor(100, random_state=0)

def independentlinear60__gbm():
    """ Gradient Boosted Trees
    """
    import xgboost

     # max_depth and learning_rate were fixed then n_estimators was chosen using a train/test split
    return xgboost.XGBRegressor(max_depth=6, n_estimators=100, learning_rate=0.1, n_jobs=8, random_state=0)

def independentlinear60__ffnn():
    """ 4-Layer Neural Network
    """
    from keras.models import Sequential
    from keras.layers import Dense

    model = Sequential()
    model.add(Dense(32, activation='relu', input_dim=60))
    model.add(Dense(20, activation='relu'))
    model.add(Dense(20, activation='relu'))
    model.add(Dense(1))

    model.compile(optimizer='adam',
                loss='mean_squared_error',
                metrics=['mean_squared_error'])

    return KerasWrap(model, 30, flatten_output=True)


def cric__lasso():
    """ Lasso Regression
    """
    model = sklearn.linear_model.LogisticRegression(penalty="l1", C=0.002)

    # we want to explain the raw probability outputs of the trees
    model.predict = lambda X: model.predict_proba(X)[:,1]
    
    return model

def cric__ridge():
    """ Ridge Regression
    """
    model = sklearn.linear_model.LogisticRegression(penalty="l2")

    # we want to explain the raw probability outputs of the trees
    model.predict = lambda X: model.predict_proba(X)[:,1]
    
    return model

def cric__decision_tree():
    """ Decision Tree
    """
    model = sklearn.tree.DecisionTreeClassifier(random_state=0, max_depth=4)

    # we want to explain the raw probability outputs of the trees
    model.predict = lambda X: model.predict_proba(X)[:,1]
    
    return model

def cric__random_forest():
    """ Random Forest
    """
    model = sklearn.ensemble.RandomForestClassifier(100, random_state=0)

    # we want to explain the raw probability outputs of the trees
    model.predict = lambda X: model.predict_proba(X)[:,1]
    
    return model

def cric__gbm():
    """ Gradient Boosted Trees
    """
    import xgboost

    # max_depth and subsample match the params used for the full cric data in the paper
    # learning_rate was set a bit higher to allow for faster runtimes
    # n_estimators was chosen based on a train/test split of the data
    model = xgboost.XGBClassifier(max_depth=5, n_estimators=400, learning_rate=0.01, subsample=0.2, n_jobs=8, random_state=0)
    
    # we want to explain the margin, not the transformed probability outputs
    model.__orig_predict = model.predict
    model.predict = lambda X: model.__orig_predict(X, output_margin=True) # pylint: disable=E1123

    return model

def cric__ffnn():
    """ 4-Layer Neural Network
    """
    from keras.models import Sequential
    from keras.layers import Dense, Dropout

    model = Sequential()
    model.add(Dense(10, activation='relu', input_dim=336))
    model.add(Dropout(0.5))
    model.add(Dense(10, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1, activation='sigmoid'))

    model.compile(optimizer='adam',
                loss='binary_crossentropy',
                metrics=['accuracy'])

    return KerasWrap(model, 30, flatten_output=True)


def human__decision_tree():
    """ Decision Tree
    """

    # build data
    N = 1000000
    M = 3
    X = np.zeros((N,M))
    X.shape
    y = np.zeros(N)
    X[0, 0] = 1
    y[0] = 8
    X[1, 1] = 1
    y[1] = 8
    X[2, 0:2] = 1
    y[2] = 4

    # fit model
    xor_model = sklearn.tree.DecisionTreeRegressor(max_depth=2)
    xor_model.fit(X, y)

    return xor_model


